High quality assurance (QA) testing has lengthy been the spine of software program improvement, however conventional QA approaches haven’t saved tempo with trendy improvement cycles and complicated UIs. Most organizations nonetheless depend on a hybrid strategy combining guide testing with script-based automation frameworks like Selenium, Cypress, and Playwright—but groups spend important quantity of their time sustaining current take a look at automation moderately than creating new exams. The issue is that conventional automation is brittle. Check scripts break with UI modifications, require specialised programming data, and sometimes present incomplete protection throughout browsers and gadgets. With many organizations actively exploring AI-driven testing workflows, present approaches are inadequate.
On this put up, we discover how agentic QA automation addresses these challenges and stroll via a sensible instance utilizing Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a pattern retail software.
Advantages of agentic QA testing
Agentic AI shifts QA testing from rule-based automation to clever, autonomous testing techniques. In contrast to typical automation that follows preprogrammed scripts, agentic AI can observe, study, adapt, and make selections in actual time. The important thing benefits embody autonomous take a look at technology via UI remark and dynamic adaptation as UI parts change—minimizing the upkeep overhead that consumes QA groups’ time. These techniques mimic human interplay patterns, ensuring testing happens from a real person perspective moderately than via inflexible, scripted pathways.
AgentCore Browser for large-scale agentic QA testing
To understand the potential of agentic AI testing at enterprise scale, organizations want strong infrastructure that may help clever, autonomous testing brokers. AgentCore Browser, a built-in software of Amazon Bedrock AgentCore, addresses this want by offering a safe, cloud-based browser surroundings particularly designed for AI brokers to work together with web sites and purposes.
AgentCore Browser consists of important enterprise safety features corresponding to session isolation, built-in observability via reside viewing, AWS CloudTrail logging, and session replay capabilities. Working inside a containerized ephemeral surroundings, every browser occasion will be shut down after use, offering clear testing states and optimum useful resource administration. For big-scale QA operations, AgentCore Browser can run a number of browser periods concurrently, so organizations can parallelize testing throughout totally different eventualities, environments, and person journeys concurrently.
Agentic QA with the Amazon Nova Act SDK
The infrastructure capabilities of AgentCore Browser grow to be actually highly effective when mixed with an agentic SDK like Amazon Nova Act. Amazon Nova Act is an AWS service that helps builders construct, deploy, and handle fleets of dependable AI brokers for automating manufacturing UI workflows. With this SDK, builders can break down advanced testing workflows into smaller, dependable instructions whereas sustaining the flexibility to name APIs and carry out direct browser manipulation as wanted. This strategy affords seamless integration of Python code all through the testing course of. Builders can interleave exams, breakpoints, and assertions immediately inside the agentic workflow, offering unprecedented management and debugging capabilities. This mixture of the AgentCore Browser cloud infrastructure with the Amazon Nova Act agentic SDK creates a complete testing ecosystem that transforms how organizations strategy high quality assurance.
Sensible implementation: Retail software testing
For instance this transformation in observe, let’s contemplate creating a brand new software for a retail firm. We’ve created a mock retail internet software to reveal the agentic QA course of, assuming the appliance is hosted on AWS infrastructure inside a personal enterprise community throughout improvement and testing phases.
To streamline the take a look at creation course of, we use Kiro, an AI-powered coding assistant to robotically generate UI take a look at instances by analyzing our software code base. Kiro examines the appliance construction, critiques current take a look at patterns, and creates complete take a look at instances following the JSON schema format required by Amazon Nova Act. By understanding the appliance’s options—together with navigation, search, filtering, and kind submissions—Kiro generates detailed take a look at steps with actions and anticipated outcomes which can be instantly executable via AgentCore Browser. This AI-assisted strategy dramatically accelerates take a look at creation whereas offering complete protection. The next demonstration exhibits Kiro producing 15 ready-to-use take a look at instances for our QA testing demo software.
After the take a look at instances are generated, they’re positioned within the take a look at information listing the place pytest robotically discovers and executes them. Every JSON take a look at file turns into an unbiased take a look at that pytest can run in parallel. The framework makes use of pytest-xdist to distribute exams throughout a number of employee processes, robotically using out there system sources for optimum efficiency.
Throughout execution, every take a look at will get its personal remoted AgentCore Browser session via the Amazon Nova Act SDK. The Amazon Nova Act agent reads the take a look at steps from the JSON file and executes them—performing actions like clicking buttons or filling varieties, then validating that anticipated outcomes happen. This data-driven strategy means groups can create complete take a look at suites by merely writing JSON information, with no need to write down Python code for every take a look at state of affairs. The parallel execution structure considerably reduces testing time. Checks that may usually run sequentially can now execute concurrently throughout a number of browser periods, with pytest managing the distribution and aggregation of outcomes. An HTML report is robotically generated utilizing pytest-html and the pytest-html-nova-act plugin, offering take a look at outcomes, screenshots, and execution logs for full visibility into the testing course of.
One of the highly effective capabilities of AgentCore Browser is its potential to run a number of browser periods concurrently, enabling true parallel take a look at execution at scale. When pytest distributes exams throughout employee processes, every take a look at spawns its personal remoted browser session within the cloud. This implies your total take a look at suite can execute concurrently moderately than ready for every take a look at to finish sequentially.
The AWS Administration Console offers full visibility into these parallel periods. As demonstrated within the following video, you’ll be able to view the lively browser periods operating concurrently, monitor their standing, and observe useful resource utilization in actual time. This observability is essential for understanding take a look at execution patterns and optimizing your testing infrastructure.
Past simply monitoring session standing, AgentCore Browser affords reside view and session replay options to observe precisely what Amazon Nova Act is doing throughout and after take a look at execution. For an lively browser session, you’ll be able to open the reside view and observe the agent interacting along with your software in actual time—clicking buttons, filling varieties, navigating pages, and validating outcomes. If you allow session replay, you’ll be able to view the recorded occasions by replaying the recorded session. This lets you validate take a look at outcomes even after the take a look at execution completes. These capabilities are invaluable for debugging take a look at failures, understanding agent habits, and gaining confidence in your automated testing course of.
For full deployment directions and entry to the pattern retail software code, AWS CloudFormation templates, and pytest testing framework, check with the accompanying GitHub repository. The repository consists of the required elements to deploy and take a look at the appliance in your personal AWS surroundings.
Conclusion
On this put up, we walked via how AgentCore Browser might help parallelize agentic QA testing for internet purposes. An agent like Amazon Nova Act can carry out automated agentic QA testing with excessive reliability.
Concerning the authors
Kosti Vasilakakis is a Principal PM at AWS on the Agentic AI workforce, the place he has led the design and improvement of a number of Bedrock AgentCore providers from the bottom up, together with Runtime, Browser, Code Interpreter, and Id. He beforehand labored on Amazon SageMaker since its early days, launching AI/ML capabilities now utilized by hundreds of corporations worldwide. Earlier in his profession, Kosti was a knowledge scientist. Exterior of labor, he builds private productiveness automations, performs tennis, and enjoys life together with his spouse and children.
Veda Raman is a Sr Options Architect for Generative AI for Amazon Nova and Agentic AI at AWS. She helps clients design and construct Agentic AI options utilizing Amazon Nova fashions and Bedrock AgentCore. She beforehand labored with clients constructing ML options utilizing Amazon SageMaker and likewise as a serverless options architect at AWS.
Omkar Nyalpelly is a Cloud Infrastructure Architect at AWS Skilled Providers with deep experience in AWS Touchdown Zones and DevOps methodologies. His present focus facilities on the intersection of cloud infrastructure and AI applied sciences—particularly leveraging Generative AI and agentic AI techniques to construct autonomous, self-managing cloud environments. By his work with enterprise clients, Omkar explores progressive approaches to scale back operational overhead whereas enhancing system reliability. Exterior of his technical pursuits, he enjoys enjoying cricket, baseball, and exploring inventive pictures. He holds an MS in Networking and Telecommunications from Southern Methodist College.
Ryan Canty is a Options Architect at Amazon AGI Labs with over 10 years of software program engineering expertise, specializing in designing and scaling enterprise software program techniques throughout a number of know-how stacks. He works with clients to leverage Amazon Nova Act, an AWS service for constructing and deploying extremely dependable AI brokers that automate UI-based workflows at scale, bridging the hole between cutting-edge AI capabilities and sensible enterprise purposes.
